Southeast Asia faces rapid growth in energy demand and continues to depend heavily on coal-based generation, creating an urgent need for renewable alternatives that can be deployed at scale. Biomass residues from agriculture represent an abundant but underutilized resource in the region. This study develops a machine learning–based spatial economic framework to quantify biomass energy potential from paddy, oil palm, cassava, and sugarcane residues across eight Southeast Asian countries and assess the feasibility of these residues for hybrid power generation. Crop yields were estimated using Random Forest regression with high-resolution (5 m) remote sensing predictors, achieving model performance achieving a maximum R2 of 0.628. Biomass residues were converted into electricity potential using crop-specific residue-to-product ratios and availability coefficients. The results show that Indonesia and Malaysia possess the highest agricultural residue potential from paddy and oil palm, while sugarcane residues exceed 20,000 MW across the region, with notable concentrations in Laos. Techno-economic modeling indicates that the levelized cost of electricity (LCOE) ranges from 0.04 to 0.11 USD/kWh, with payback times of 20–130 months, demonstrating cost competitiveness with coal, especially when the monetized cost of CO2 emissions are included. Spatial hybrid integration analysis reveals that paddy-rich corridors near existing coal plants have the strongest potential for biomass co-firing and hybridization. The proposed framework provides a scalable methodology for regional biomass planning and offers practical insights for policymakers in accelerating renewable energy transition and reducing fossil fuel dependence in Southeast Asia. © 2026 The Author(s)